1. Field of the Invention
This invention relates to a method of processing three-dimensional object data, and more particularly to a method of searching for and reading out data of objects contained in a space for processing relevant to rendering from a storage unit, when three-dimensional data for such objects is previously stored in the storage unit.
2. Description of the Related Art
In the world of three dimensional computer graphics (CG), three dimensional objects are often rendered according to the user's eyepoint. For example, when rendering an image of buildings from above, a user must first specify parameters designating user's eyepoint and line-of-sight. These parameters determine a visual space called a view volume. Next, three-dimensional data on the buildings and other objects is read out from a storage for viewing transformation in which coordinates are transformed, followed by a clipping operation in which objects outside the view volume are removed. Rasterizing (rendering) processing is performed on the objects not removed by clipping and colors are applied on them. The objects are then displayed on the screen.
Some CG applications, such as a driving simulation or a walk-through simulation in which images are constantly changing according to the user's eyepoint, require that the coordinates of all the objects be re-calculated each time the eyepoint changes. This makes it impossible for real-time processing to be done smoothly when the number of objects is large. Although computers are becoming more and more powerful, there is a tendency for the speed required by CG applications to exceed the speed of the computer, making computer processing speed a major bottleneck in three-dimensional CG processing. In particular, when, for example, a driving simulation must cover all the objects in a town or when a walk-through video of a factory with various facilities is created, a huge number of objects is involved. Three-dimensional real-time data simulation cannot be done using a conventional method when the amount of data is this large.